In partnership with Intel, Vectara Reduces LLM Hallucinations for a Secure and Superior. Enterprise AI RAG-based solution for reliable GenAI agents and assistants. Utilize Vectara’s HHEM model to optimise RAG applications. For more accurate and trustworthy reports, assess and lessen LLM hallucinations.
In artificial intelligence (AI) models, especially large language models (LLMs), hallucinations occur when the model produces information that appears to be logical but is, in fact, inaccurate or deceptive given the input circumstances. Inaccurate information, illogical claims, or made-up details are a few of the findings that point to hallucinations.
For reliable AI solutions, particularly in large-scale enterprise models, hallucinations must be addressed. During a recent webinar with Intel, Vectara specialists talked about how their hallucination detection model and agentic Retrieval Augmented Generation (RAG) based technology assist efficiently address hallucinations in enterprise LLMs at scale.
Webinar Topics
- The idea of hallucinations in LLMs, causes of the problem, and strategies for reducing it.
- How hallucinations are addressed using Vectara’s Hughes Hallucination Evaluation Model (HHEM Model) and RAG-as-a-service platform. and
- How to detect LLM hallucinations in practical situations.
Vectara’s Vision: Reliable, Secure, and Explainable AI
- Vectara offers an AI framework platform that makes it possible to create trustworthy and accountable AI helpers and agents.
- It seeks to guarantee.
- outcomes that are high-precision and accurate, outcomes that are secure, and exploitability the capacity to demonstrate how the AI model arrived at the conclusions it produced.
Large language models (LLMs) can be deceived almost as easily as people, making them vulnerable to quick attacks. For example, you can fool an LLM into disclosing someone’s income or other information that is prohibited by giving them the PIN code for their bank account.
Along with these security risks, LLMs have a propensity to hallucinate, which means that they may produce inaccurate or irrelevant answers depending on what the model learnt from the training data. Consider asking an LLM, “What is the interest rate for a 20-year fixed mortgage?” as a straightforward illustration of how an LLM could have hallucinations.
The model might give you an x% interest rate in response to your prompt, depending on the data it was trained on. The practical response to the challenge, however, ought to mention that mortgage interest rates differ according to a number of variables, including credit ratings and market conditions. In the industry’s large-scale AI solutions, such deceptive answers that diverge from real-world situations are undesired.
Utilize Retrieval Augmented Generation (RAG) to Reduce LLM Hallucinations
By obtaining data from other sources, such a database, before producing a result, a technique known as Retrieval Augmented Generation (RAG) allows LLMs to produce precise answers pertinent to the input scenario. Instead of depending solely on the original training data, the model produces more logical output because it is aware of the most recent information.
For both small and large company LLMs, Vectara offers a RAG-as-a-service platform that helps minimize or eradicate hallucinations. It helps lessen LLM hallucinations and lets you feed LLM your own data. Strict role-based and entity-based control mechanisms on the platform guarantee data security by preventing data leaks. Furthermore, it adds a degree of exploitability to the LLM by providing citations or references to the sources of responses.
Your input data is initially routed to a data store (text or vector database), as illustrated in Fig. 1 below. The LLM bases its response to the user’s inquiry on information from the data store during the information retrieval step. To guarantee correct outcomes, the LLM reacts based on real-time information rather than assuming anything based on preset training data.
Detection of LLM Hallucinations Made Easy
The speaker illustrates in the webinar from [00:13:35] how hard it is to identify LLM hallucinations and how they might be classified as “unwanted” or “intrinsic,” “benign,” or “questionable” depending on the ambiguity of the generated reaction.
To identify LLM hallucinations, Vectara created its own Hughes Hallucination Evaluation Model (HHEM model), which is accessible on Hugging Face. In RAG applications that summaries facts, the HHEM model series is especially useful for detecting hallucinations. The output summary’s conformity with the input facts is verified by the HHEM Model. To determine their hallucination rates, a number of LLMs in the market are assessed using a specific dataset on the HHEM model leaderboard. Intel’s neural-chat-7b model ranks extremely high on the leaderboard because it exhibits remarkably low hallucination rates.
Leveraging Agentic RAG for Enterprise AI
Vectara’s platform’s primary GenAI application cases include AI agents and assistants (both conversational and question-answering). The following categories apply to AI agents:
- Complex tasks like decomposing the input query into several questions, responding to each one, and combining all of the answers can be completed by an agentic RAG.
- Action engines that can carry out a task for you directly, such sending emails or publishing pages on a website.
What Comes Next?
See how Vectara’s solutions address LLM hallucinations in both basic and sophisticated commercial LLMs by watching the entire webinar recording. Accelerate and improve AI development with Intel’s wide range of AI software, which includes a large number of optimized tools, libraries, and frameworks driven by the oneAPI programming architecture.
You may test out Intel AI software optimizations on the newest accelerated hardware, including CPUs, GPUs, AI PC NPUs, and the Intel Gaudi AI accelerator, which is accessible on the Intel Tiber AI Cloud platform, in addition to installing developer tools from it AI Tools Selector.
